Robotics: Biological Hypercomputation and Bio-Inspired Swarms Intelligence

Abstract

The concept of hyper computing (BH) has been introduced to understand how living systems process information. This article presents the BH developments but supports the idea that living beings communicate in structures and not in the mode of signs and symbols. Genetic algorithms are bio-inspired optimization algorithms that simulate the process of the natural evolution of species. They make it possible to manipulate a set of solutions through several iterations to converge towards optimal solutions. This work allows us to study the efficiency of genetic algorithms for statistical machine translation. The bio-inspired communitarian literature proposes a communication model to capture the nature of individuals’ interaction. The research-based on metrics, topology, and algorithms are derived from the bio-inspired communication model’s visual investigation. The evaluation’s assumption is the choice of biologically inspired communication models can influence group performance for a specific task. The communication model was evaluated in two environments. Swarm mission: search for targets communicated among others and avoid opponents. Overall results of the survey Prove that the group agent has the best overall performance when used a bio-inspired communication model to look for specific tasks and avoid compliance with best practices than hostile tasks when using topology models. This study’s main cause is to magnify the group’s mission’s performance by deliberately selecting the bio-inspired communication model.